Schemas, pipelines, and warehouses built for reliability — and ready to feed BI dashboards, ML models, and AI features.
OLTP schemas designed for the actual workload — not over-normalized, not chaos.
BigQuery, Snowflake, Redshift, or Postgres-as-warehouse — sized for your team and budget.
Batch and streaming pipelines that are reproducible, observable, and easy to debug.
Feature stores, embeddings, and curated datasets that make AI projects plausible, not painful.
Relational and document modeling, normalization tradeoffs, and migration patterns.
dbt, Airflow, Dagster, and managed services — chosen for the team that has to maintain them.
Layered architectures (raw / staging / mart) with clear ownership and SLAs.
Tests, data contracts, and lineage so analysts and AI systems can trust the inputs.
Most teams don't need a Big Tech data platform. They need pipelines that don't break, schemas that survive growth, and a warehouse that answers questions in seconds.
OLTP, OLAP, vector, and object stores — matched to access patterns and budget.
Access controls, PII handling, and data retention that satisfy security and legal.
Partitioning, clustering, and caching so queries stay fast as data grows.
Map sources, models, pipelines, and pain points.
Target architecture sized for the actual workload and team.
Implement pipelines, tests, and observability with phased migration.
Document, train, and either step back or stay on as a partner.
Connect SaaS tools, custom systems, and legacy platforms so data flows smoothly.
DesignInterfaces and product flows that make complex workflows — and AI — feel simple.
QAConfidence in every release, including AI features.
A focused data audit usually surfaces the few changes that pay back fastest — and the ones that don't.
Tell us about your project